Jarkko Salojärvi, Samuel Kaski and Janne Sinkkonen. Discriminative clustering in Fisher metrics.  In: O. Kaynak, E. Alpaydin, E. Oja, L. Xu, editors, Artificial Neural Networks and Neural Information Processing - Supplementary proceedings ICANN/ICONIP 2003,  Istanbul, Turkey, June, pp. 161-164. (postscript, gzipped postscript)

Discriminative clustering (DC) finds a Voronoi partitioning of a primary data space that, while consisting of local partitions, simultaneously maximizes information about auxiliary data categories. DC is useful in exploration and in finding more coarse or refined versions of already existing categories.  Theoretical results suggest that Voronoi partitions in the so-called Fisher metric would outperform partitions in the Euclidean metric. Here we use a local quadratic approximation of the Fisher metric, derived from a conditional density estimator, in defining the partitions and show that the resulting algorithms outperform the conventional variants.